DATA MANIPULATION

p {
    line-height: 22px;
}

h1{
  margin-bottom: -20px;
}

h2{
  margin-bottom: -11px;
}

h3{
  margin-bottom: -10px;
}

.emphasized {
   font-size: 1.2em;
   font-family: Nunito;
}

.greenemph {
   font-size: 1.2em;
   font-family: Nunito;
   color: #69995D
}

.greenhead {
   font-size: 35px;
   font-family: Nunito;
   color: #69995D
}

.nunito {
   font-size: 25px;
   font-family: Nunito;
}

.nunitosm {
   font-family: Nunito;
}
.nunitosmgrey {
   font-family: Nunito;
   color: #E8E9E8
}

.invisible {
   color: white;
}

.remark-code {
background: #green;
}
.remark-slide-number {
  font-size: 10pt;
  margin-bottom: -11.6px;
  margin-right: 10px;
  color: #FFFFFF; /* white */
  opacity: 0; /* default: 0.5 */
}

</style>

.center[
# DATA MANIPULATION
.nunito[LECTURE 1]
.greenhead[INTRO TO R PROGRAMMING]

**MARIA MONTOYA-AGUIRRE**

M1 APE @ PARIS SCHOOL OF ECONOMICS
]

---
 
##WELCOME TO THE COURSE!
.pull-left[
**ABOUT ME**: 
- PhD candidate at Paris School of Economics. Started APE in 2019! 
- Working on (labor ∩ crime ∩ development) economics 
- I've used R for +5 years now. 
]

.pull-right[
**SOME ADVICE FOR M1**: 
- Work consistently 
- Take care of yourself 
- Stay connected to the research
- Look for the questions/problems you care about 
]
 
 
**Econ podcasts I like:**

---
class:middle inverse
.center[
.nunito[
# ABOUT YOU

WHAT IS YOUR NAME?
 
 WHERE ARE YOU FROM?
 
 ONE OF YOUR FAVORITE PROBLEMS
 
]]
---
## ABOUT THE COURSE
.emphasized[MAIN OBJECTIVE]

Learning R programming to carry out your empirical homeworks and research projects.

.emphasized[STRUCTURE]

4 sessions (2h each) designed to introduce you to the main steps in the data analysis (~ empirical research) pipeline:
0. Data collection
1. Data cleaning and tidying **(Today!)**
2. Data analysis
  - Data visualization **(Lecture 2)**
  - Econometrics **(Lecture 4)**
3. Report results & publish code  **(Lecture 3)**

.emphasized[COURSE WEBSITE]

https://github.com/mariamontoyaa/2023-intro-to-R-public

🚨 Read the class policies and instructions for asking for help. 
 
 
---
# AGENDA
.nunitosm[
.pull-left[
- GETTING STARTED WITH R
  - THE R STUDIO IDE
  - OBJECTS AND FUNCTIONS
  - IMPORTING DATA

- ANATOMY OF A DATA FRAME
  - SCALARS
  - VECTORS
  - DATAFRAMES
  - SUBSETTING
]

.pull-right[
- MANIPULATING DATA WITH {dplyr}
  - PACKAGES
  - MAIN FUNCTIONS
  - `group_by()` vs. `summarise()`

- THE DATA ANALYSIS PIPELINE
  - A CHECKLIST FOR CLEANING DATA
  - LOOKING FOR HELP
]
 ]
---

### 💬 A Note on AI Tools

You’ll soon see that tools like ChatGPT  
can be **incredibly helpful** for learning R.

But **today**, we’re focusing on **foundations** —  
how to read code, write it, and think through problems.

⏳**For Today**

> **Please don’t use AI during this session.**  
> Let your brain do the debugging !

Why?

-  Struggling helps you learn  
-  You’re building foundations that will make AI more useful later  
- ️ We’ll talk about AI **at the end of class**

🧭**What to Do Instead**
- Use the documentation in R
- Read error messages carefully  
- Ask a neighbor or raise your hand

---

## GETTING STARTED WITH R
Programming language and software environment for statistical computing and graphics. Widely used in Economics, Statistics and Biostatistics.

.pull-left[
.greenemph[WHY SHOULD WE LEARN TO CODE?]

Transparent, shareable and reproducible analysis

]
 .pull-right[
<img src="data:image/png;base64,#inputs/01_learningr.png" width="1600" />
 ]
---
 
## GETTING STARTED WITH R
Programming language and software environment for statistical computing and graphics. Widely used in Economics, Statistics and Biostatistics.

.pull-left[
.greenemph[WHY SHOULD WE LEARN TO CODE?]

Transparent, shareable and reproducible analysis
 
.greenemph[WHY CHOOSE **R**?] 
 - Free and open source. Development of features and fixes is fast
 - Less specialized
 - Easy to Google
 - Pretty graphs (and [accidental art](https://twitter.com/accidental__aRt))
 - Super versatile: pretty graphs, GIS integration for maps, web scrapping, machine learning, markdown, dashboards

]
 .pull-right[
<img src="data:image/png;base64,#inputs/01_learningr.png" width="1600" />
 ]
---
### THE **R** STUDIO **IDE** 
An **Integrated Development Environment** provides the comprehensive facilities for programmers in an application

<img src="data:image/png;base64,#inputs/01_rstudio_ide.png" width="90%" />
---
### THE **R** STUDIO **IDE** 
An **Integrated Development Environment** provides the comprehensive facilities for programmers in an application

<img src="data:image/png;base64,#inputs/01_rstudio-ide-parts.jpg" width="90%" />
---
### SOURCE
.nunito[You'll spend most of you time here!] Where you write and save code 
--
(File > New File > R Script) 
📌 **First thing:** Save your script! 
--

--
- Run and source buttons send code to the console to be executed 
  - Run (from selection). Highlight the code  → `Ctrl + Enter`. If no code is selected it will run the line where the cursor is. 
  - Source (run everything)

```r
1 + 1 # Equals two?

1 +   # Also equals two? 
  1   # R recognizes that you are not done yet! 
      
1
+ 1   # Equals one. 
```
---
### SOURCE
.nunito[You'll spend most of you time here!] Where you write and save code  
(File > New File > R Script) 
📌 **First thing:** Save your script!

- An `#` allows to comment your code. Everything after it is ignored until a line break
- Separate different commands with a **line break** for clarity. But, note that a space doesn't break a command. 
- Run and source buttons send code to the console to be executed 
  - Run (from selection). Highlight the code  → `Ctrl + Enter`. If no code is selected it will run the line where the cursor is. 
  - Source (run everything)

```r
1 + 1 # Equals two?
```

```
## [1] 2
```

```r
1 +   # Also equals two? 
  1   # R recognizes that you are not done yet! 
```

```
## [1] 2
```

```r
1
```

```
## [1] 1
```

```r
+ 1   # Equals one. 
```

```
## [1] 1
```
---
 
 
 
 
 
### CONSOLE
.nunito[The heart of the operation 🫀]

Here is where you communicate with R to get it to do things.

--
- You write after the `>` prompt. It means that R is ready for a new instruction. Press enter and R will execute the code.
- You can write there directly for quick calculations or fleeting requests you don't need to save.

.greenemph[**WRITE MOST OF YOUR CODE IN THE SOURCE PANEL**]

---
 
 
### ENVIRONMENT
.nunito[Where we keep track of things 🖇️]

- Data analysis requires manipulating different *objects*: datasets, vectors, functions, etc. Here is where we keep track of them.

--
- R is an **object-oriented** language. We'll talk about this in a bit. What it means is that we can see everything we are working with and things are only stored when we ask to.

--
- We **assign** a value to an object with `<-` the assignment operator

--
**See what happens in the environment panel when we run this:**

```r
5 + 1
x <- 5 # Assigning the value 
x
x + 1
x
x <- x + 1 # Assigning a new value 
x 
```

---
 
### ENVIRONMENT
.nunito[Where we keep track of things 🖇️]

- Data analysis requires manipulating different *objects*: datasets, vectors, functions, etc. Here is where we keep track of them. 
- R is an **object-oriented** language. We'll talk about this in a bit. What it means is that we can see everything we are working with and things are only stored when we ask to.
- We **assign** a value to an object with `<-` the assignment operator

**See what happens in the environment panel when we run this:**

```r
5 + 1
```

```
## [1] 6
```

```r
x <- 5 # Assigning the value 
x
```

```
## [1] 5
```

```r
x + 1
```

```
## [1] 6
```

```r
x
```

```
## [1] 5
```

```r
x <- x + 1 # Assigning a new value 
x 
```

```
## [1] 6
```

---
class: middle
### FILES/ PLOTS/ ... PANEL
.nunito[Many helpful things here 🗄️]
- Files shows your *working directory*, the default location of any files you read into or save out of R  `getwd()`
  - We'll see more about how to work with directories and R Projects in **Lecture 3**

--
- Plots / Viewer 
- Packages
- Help 
  - Add a `?` in front of any function to get help: `?getwd()`

---
## OBJECTS AND FUNCTIONS
R revolves around 2 things: **objects and functions** (things and procedures?)

.pull-left[
- **Objects** are vectors, dataframes, regression models. They have attributes. 
 
- **Functions** are procedures that typically take one or more objects as arguments (inputs), do something with them and return a new object

]

.pull-right[

```r
# This is not R code 
window <- "This is a window"
door <- "This is a door" 
one_hundred <- 100
two_thousand <- 2000
```

```r
# This is not R code
open(window)
open(door)
open(book)

clean(toilet)
clean(data)
clean(conscience)
```
]
--
 
.center[
.nunito[Programming in R is defining objects and applying functions to those objects ad infinitum]
]

```r
# This is R code 
grades <- c(15, 10, 12, 13, 18)
mean(grades) 
```

```
## [1] 13.6
```
---
## PACKAGES

.nunito[There are **tons** of user-created functions, they come bundled in ***packages***]

- Some functions are built into R itself — this is called the **{base}** package.
- Most extra functions live in packages hosted on the **Comprehensive R Archive Network (CRAN)**.

--  
 
 
.nunito[💡 A package is like a lightbulb]

You need to:
- **Install** it once: `install.packages("package_name")`
- **Turn it on** every time you need it: `library(package_name)`

**You can also call a function without loading the package**: 
 `package_name::function` **Advantage**: clear for others which package is it coming from, avoids conflicts when packages have functions with the same name. **Disadvantage:** Not practical for functions you use often.
 
---

class:middle inverse
.center[
.nunito[
# NOW WE ARE GOING TO WORK WITH DATA

BUT FIRST,
LET'S GET OUR FILES ORGANIZED
 
]]
---

.greenhead[**FILE PATHS, FOLDERS & PROJECTS**]

**🗺️ What's a file path?**

It's like directions to a file on your computer. R needs to know where to find your data. 
- **Absolute** paths:   
`"C:/Users/Maria/Documents/myproject/data/myfile.csv"` Windows
`"/Users/maria/Documents/myproject/data/myfile.csv"` Mac 
- **Relative paths:** `"data/myfile.csv"`

**📁Recommended folder structure**

```r
myproject/               ← 📁 Your project folder
├── data/                ← data files (CSV, DTA, etc.)
├── scripts/             ← R scripts 
├── output/              ← tables, figures, model results
├── myproject.Rproj      ← your R Project file
```

---

.greenhead[**FILE PATHS, FOLDERS & PROJECTS**]

**🗺️ What's a file path?** 
- **Absolute** paths:   
`"C:/Users/Maria/Documents/myproject/data/myfile.csv"` Windows
`"/Users/maria/Documents/myproject/data/myfile.csv"` Mac 
- **Relative paths:** `"data/myfile.csv"`

**📁Recommended folder structure**
<img src="data:image/png;base64,#inputs/01_myproject_screenshot.png" width="1171" />

---

.greenhead[**FILE PATHS, FOLDERS & PROJECTS**]

**📦Create an R Project in RStudio** 
1. **File -> New Project** 
2. Select **New Directory** or **Existing Directory**
3. Name the project (e.g. `intro_to_R`)
4. Open the `.Rproj` file every time you work

📍**Using `here()` for Clean File Paths**

```r
install.packages("here")  # Install the package
library(here)              # Load the package
here("data", "myfile.csv") # Relative path 
"C:/Users/Maria/Documents/myproject/data/myfile.csv" # Absolute path
```

---

.greenhead[**IMPORTING AND INSPECTING DATA**]

📥 **Download the data folder from the course website** 
- We are going to analyze the 250 top rated French Movies in IMDB
- Open the CSV file in your computer and inspect it: columns are variables and rows are observations

---

.greenhead[**IMPORTING AND INSPECTING DATA**]

.nunito[How do we get this data into R?]
- We import it using *read* functions, which take a file path as an input and give the file content as an output.

🚨 Use forward slashes `"mycomputer/folder/file.csv"` instead of backward slashes `"mycomputer\folder\file.csv"`

```r
imdb <- read.csv(here("data", "01_imdb-top250-french.csv"))

# Exploring the data 
head(imdb, 4)   # Show first four rows
tail(imdb, 3)   # Show last three rows
View(imdb)      # Show data in a spreadsheet view
```
---

.greenhead[**DID WE DO IT RIGHT?**]

---
 
 
.greenhead[**DID WE DO IT RIGHT?**]

---
 
 
### USING FUNCTIONS
- Read the documentation! `?read.csv()`
 - Description
 - Usage 
 - Arguments: with and without default values (you need to write these to work)
 - ⭐️ Examples

```r
read.csv(file, header = TRUE, sep = ",", quote = "\"",
         dec = ".", fill = TRUE, comment.char = "", ...)
```
- You don’t need to write argument names if you write the arguments in the correct order

---
 
 
### SO, WHAT HAPPENED?
.nunito[A common problem when importing data is that the *character encoding* is handled incorrectly. ]

This is when we see weird characters like `Ã©` where letters should be.

All the characters we see, like `1` ,`#` or `M`, are produced by a sequence of bits (1-0), the encoding determines how these bits are translated into characters.

We need to make sure we are using the right *translation*!

```r
imdb <- read.csv(here("data", "01_imdb-top250-french.csv"),
* encoding = "UTF-8") #UTF-8 should work for latin languages
```

---
### WHEN THINGS DON'T GO WELL... DEBUGGING🐞
**Go line by line making sure each command does what you expect it to. **

Most of the time the problem is one of these:

--
- **R isn't ready:** The `>` should appear in the console. There is a `+` instead. You wrote something *incomplete* before and R is waiting for you to finish. Press `ESC`. ` c(1, 3, `

--
- **Mispelled object or function** Letter reversals or capitalization are often the issue. `maen(1, 2, 3)`, `Mean(1, 2, 3)`

--
- **Punctuation problems** Mixing commas `,` and periods `.` or incorrect use of `" "`. 
 `c(1,2.3)`

--
- **Wrong argument**

--
- **Missing arguments** 
--

.center[.nunito[**🌈 READ CAREFULLY 🌈**]]

---
class: inverse
## EXERCISE
I want to:
- Create a vector with 20 numbers drawn from a normal distribution with a mean of 5 and standard deviation of 2. 
- Obtain its median.

.nunito[Fix the following code.

🔍 Start with `?rnorm()`]

.pull-left[

```r
v1 <- rnorm(2, 20)

Median(v1)
```

]

---
# AGENDA
.nunitosm[

.pull-left[
.nunitosmgrey[
- GETTING STARTED WITH R
  - THE R STUDIO IDE
  - OBJECTS AND FUNCTIONS
  - IMPORTING DATA
]
- ANATOMY OF A DATA FRAME
  - SCALARS
  - VECTORS
  - DATAFRAMES
  - SUBSETTING
]

.pull-right[
- MANIPULATING DATA WITH {dplyr}
  - PACKAGES
  - MAIN FUNCTIONS
  - `group_by()` vs. `summarise()`

- THE DATA ANALYSIS PIPELINE
  - A CHECKLIST FOR CLEANING DATA
  - LOOKING FOR HELP
]
 ]
---

class: split-three

## ANATOMY OF A DATA FRAME

What makes up a dataset? The smallest unit of data:

.nunito[SCALARS]

---
class: split-three

## ANATOMY OF A DATA FRAME

What makes up a dataset? The smallest unit of data:

.nunito[SCALARS]

.column[
 
 
 
 
 
 
 
 
.center[
*Numeric*]

```r
a <- 100
b <- 3 / 100
c <- (a + b) / b 
```
Try: `class(a)`

- *int:* round numbers *dbl:* 2 decimals
]

.column[
 
 
 
 
 
 
 
 
.center[
*Character*]

```r
d <- "window"
e <- "4"
f <- "This is a room" 
```
Try: `class(d)`

- Also called *strings* 
]

.column[
 
 
 
 
 
 
 
 
.center[
*Boolean*]

```r
g <- TRUE
h <- (3 >= 4) 
i <- (c == (a+b) / b)
```
Try: `class(g)`

- Produced with operators `== > < >= <= & | !` 
]
 
 
 
 
 
 
 
 
 
 
 
---
class: split-three

## ANATOMY OF A DATA FRAME

What makes up a dataset? The smallest unit of data:

.nunito[SCALARS]

.column[
 
 
 
 
 
 
 
 
.center[
*Numeric*]

```r
a <- 100
b <- 3 / 100
c <- (a + b) / b 
```
Try: `class(a)`

- *int:* round numbers *dbl:* 2 decimals
]

.column[
 
 
 
 
 
 
 
 
.center[
*Character*]

```r
d <- "window"
e <- "4"
f <- "This is a room" 
```
Try: `class(d)`

- Also called *strings* 
]

.column[
 
 
 
 
 
 
 
 
.center[
*Boolean*]

```r
g <- TRUE
h <- (3 >= 4) 
i <- (c == (a+b) / b)
```
Try: `class(g)`

- Produced with operators `== > < >= <= & | !` 
]
 
 
 
 
 
 
 
 
 
 
 
.center[
.greenemph[What happens if we execute this?]

]

```r
a + b
a + e           
a + as.numeric(e) 
as.numeric(d)    
as.character(a)   
```
---
class: split-three

## ANATOMY OF A DATA FRAME

What makes up a dataset? The smallest unit of data:

.nunito[SCALARS]

.column[
 
 
 
 
 
 
 
 
.center[
*Numeric*]

```r
a <- 100
b <- 3 / 100
c <- (a + b) / b 
```
Try: `class(a)`

- *int:* round numbers *dbl:* 2 decimals
]

.column[
 
 
 
 
 
 
 
 
.center[
*Character*]

```r
d <- "window"
e <- "4"
f <- "This is a room" 
```
Try: `class(d)`

- Also called *strings* 
]

.column[
 
 
 
 
 
 
 
 
.center[
*Boolean*]

```r
g <- TRUE
h <- (3 >= 4) 
i <- (c == (a+b) / b)
```
Try: `class(g)`

- Produced with operators `== > < >= <= & | !` 
]
 
 
 
 
 
 
 
 
 
 
 
.center[
.greenemph[What happens if we execute this?]

]

```r
a + b
a + e             # Error in a + e : non-numeric argument to binary operator
a + as.numeric(e) 
as.numeric(d)     # Character is coerced to NA (not available)
as.character(a)   
```

---
.center[
### DATA STRUCTURE
<img src="data:image/png;base64,#inputs/01_data_structure.png" width="1365" />
]
---
### VECTORS 
Unidimensional object that stores a sequence of values of the **same type**

```r
c("Hello world", 35, FALSE)     
```

```
## [1] "Hello world" "35"          "FALSE"
```
--

```r
c("Hello world", as.character(35), as.character(FALSE))  # Equivalent to this
```

```
## [1] "Hello world" "35"          "FALSE"
```
--

.nunito[Having the same type of values allows operations to apply to all elements in the vector]

.pull-left[

```r
v1 <- c(1, 2, 3)
v1
v1 / 3 
v1 * v1 # Multiply element wise
v1 %*% v1 # Matrix multiplication
```
]

.pull-right[ 
]
---
### VECTORS 
Unidimensional object that stores a sequence of values of the **same type**

```r
c("Hello world", 35, FALSE)     
```

```
## [1] "Hello world" "35"          "FALSE"
```

```r
c("Hello world", as.character(35), as.character(FALSE))  # Equivalent to this
```

```
## [1] "Hello world" "35"          "FALSE"
```

.nunito[Having the same type of values allows operations to apply to all elements in the vector]

.pull-left[

```r
v1 <- c(1, 2, 3)
v1
```

```
## [1] 1 2 3
```

```r
v1 / 3 
```

```
## [1] 0.3333333 0.6666667 1.0000000
```

```r
v1 * v1      # Multiply element wise
```

```
## [1] 1 4 9
```

```r
v1 %*% v1    # Matrix multiplication
```

```
##      [,1]
## [1,]   14
```
]

.pull-right[

]
---
### VECTORS 
Unidimensional object that stores a sequence of values of the **same type**

```r
c("Hello world", 35, FALSE)     
```

```
## [1] "Hello world" "35"          "FALSE"
```

```r
c("Hello world", as.character(35), as.character(FALSE))  # Equivalent to this
```

```
## [1] "Hello world" "35"          "FALSE"
```

.nunito[Having the same type of values allows operations to apply to all elements in the vector]

.pull-left[

```r
v1 <- c(1, 2, 3)
v1
```

```
## [1] 1 2 3
```

```r
v1 / 3 
```

```
## [1] 0.3333333 0.6666667 1.0000000
```

```r
v1 * v1      # Multiply element wise
```

```
## [1] 1 4 9
```

```r
v1 %*% v1    # Matrix multiplication
```

```
##      [,1]
## [1,]   14
```
]

.pull-right[

```r
# Creating vectors
v1 <- c(1, 1, 3, 5) # Specify all 
v1
v2 <- c(1:5) # Evenly spaced 
v2
v3 <- c(v1, v2) # Combining 
v3
```
]
---
### VECTORS 
Unidimensional object that stores a sequence of values of the **same type**

```r
c("Hello world", 35, FALSE)     
```

```
## [1] "Hello world" "35"          "FALSE"
```

```r
c("Hello world", as.character(35), as.character(FALSE))  # Equivalent to this
```

```
## [1] "Hello world" "35"          "FALSE"
```

.nunito[Having the same type of values allows operations to apply to all elements in the vector]

.pull-left[

```r
v1 <- c(1, 2, 3)
v1
```

```
## [1] 1 2 3
```

```r
v1 / 3 
```

```
## [1] 0.3333333 0.6666667 1.0000000
```

```r
v1 * v1      # Multiply element wise
```

```
## [1] 1 4 9
```

```r
v1 %*% v1    # Matrix multiplication
```

```
##      [,1]
## [1,]   14
```
]

.pull-right[

```r
# Creating vectors
v1 <- c(1, 1, 3, 5) # Specify all 
v1
```

```
## [1] 1 1 3 5
```

```r
v2 <- c(1:5) # Evenly spaced 
v2
```

```
## [1] 1 2 3 4 5
```

```r
v3 <- c(v1, v2) # Combining 
v3
```

```
## [1] 1 1 3 5 1 2 3 4 5
```
]
---
.center[
### DATA STRUCTURE
<img src="data:image/png;base64,#inputs/01_data_structure.png" width="1365" />
]
---

### DATA FRAMES AND SUBSETTING

--
Let's explore the dataset with function `str()`

```r
str(imdb)
```

```
## 'data.frame':	250 obs. of  13 variables:
##  $ X             : int  1 2 3 4 5 6 7 8 9 10 ...
##  $ Name          : chr  "Shoah" "Home" "Untouchable" "Le Trou" ...
##  $ Rank          : int  1 2 3 4 5 6 7 8 9 10 ...
##  $ Year          : chr  "-1985" "(I) (2009)" "-2011" "-1960" ...
....
```

**A dataframe is a collection of vectors.** We use `$` to select a variable of the dataset:

```r
imdb$Name
```

```
##   [1] "Shoah"                                               
##   [2] "Home"                                                
##   [3] "Untouchable"                                         
....
```
--
We can also use `$` to create and replace variables

```r
imdb$new_var <- c(250:1) # The vector has to be of the same length as the dataframe
```

---
### DATA FRAMES AND SUBSETTING
We can also use the **subsetting** operator `[]` to select a portion of our data. Like this: `data[row, column]` or `vector[element]`.

.pull-left[
We indicate what we want to select using:

- Indices (position) 
  - *The second column*
- Logical operators 
  - *Movies ranked above 10*
- Names: 
  - *Columns "Rank" and "Duration"*
]

.pull-right[
**First row and cols "Name" and "Year": **

```r
imdb[1, c("Name", "Year")]
imdb[1, c(2, 4)]
```

]
---

### DATA FRAMES AND SUBSETTING
We can also use the **subsetting** operator `[]` to select a portion of our data. Like this: `data[row, column]` or `vector[element]`.

.pull-left[
We indicate what we want to select using:

- Indices (position) 
  - *The second column*
- Logical operators 
  - *Movies ranked above 10*
- Names: 
  - *Columns "Rank" and "Duration"*
]

.pull-right[
**First row and cols "Name" and "Year": **

```r
imdb[1, c("Name", "Year")]
imdb[1, c(2, 4)]
```
**First 10 rows, all columns:**

```r
imdb[1:10,] 
imdb[-(11:250),]
```

]
---

### DATA FRAMES AND SUBSETTING
We can also use the **subsetting** operator `[]` to select a portion of our data. Like this: `data[row, column]` or `vector[element]`.

.pull-left[
We indicate what we want to select using:

- Indices (position) 
  - *The second column*
- Logical operators 
  - *Movies ranked above 10*
- Names: 
  - *Columns "Rank" and "Duration"*
]

.pull-right[
**First row and cols "Name" and "Year": **

```r
imdb[1, c("Name", "Year")]
imdb[1, c(2, 4)]
```
**First 10 rows, all columns:**

```r
imdb[1:10,] 
imdb[-(11:250),]
```

**Name of the movies ranked 11-14 :**

```r
imdb[Rank < 15 & Rank > 10, "Name" ] 
 # This doesn't work. Why?
imdb[imdb$Rank < 15 & 
 imdb$Rank > 10, "Name"]
```
]
---
class: inverse
 
 
 
### EXERCISE
- Create the vector `c(7, 7, 8, 8, 7, 7, 8, 8)` using the function `rep()`

- Modify the code `seq(1, 6)` to get the vector c(0, 2, 4, 6)

- Get the name of the shortest movie in the data set 
 🔍 Use functions `min()`, `substr()`

- There are 6 movies that last 104 minutes, what are they called?

--
<div class="countdown" id="timer_2594511c" data-update-every="1" tabindex="0" style="right:0;bottom:0;">
<div class="countdown-controls"><button class="countdown-bump-down">−</button><button class="countdown-bump-up">+</button></div>
<code class="countdown-time">10:00</code>
</div>

---
# AGENDA
.nunitosm[

.pull-left[
.nunitosmgrey[
- GETTING STARTED WITH R
  - THE R STUDIO IDE
  - OBJECTS AND FUNCTIONS
  - IMPORTING DATA

- ANATOMY OF A DATA FRAME
  - SCALARS
  - VECTORS
  - DATAFRAMES
  - SUBSETTING
]]

.pull-right[
- MANIPULATING DATA WITH {dplyr}
  - PACKAGES
  - MAIN FUNCTIONS
  - `group_by()` vs. `summarise()`

- THE DATA ANALYSIS PIPELINE
  - A CHECKLIST FOR CLEANING DATA
  - LOOKING FOR HELP
]
 ]

---
### MANIPULATING DATA WITH {dplyr}

.nunito[There are tooons of user-created functions, which come in ***packages***]

- So far we’ve only used functions that are already built in R, also known as ****{base}****
    - Packages are centralized on the Comprehensive R Archive Newtork (CRAN)

--
 
 
- An R package is like a lightbulb 💡. First you need to install it `install.packages("package_name")`, and then turn it on when you need it `library("package_name")` 
--
 
 - You can avoid loading the packages by specifying the package `package_name::function` **Advantage**: clear for others which package is it coming from, avoids conflicts when packages have functions with the same name. **Disadvantage:** Not practical for functions you use often.
 
--

- The 🌈[tidyverse](tidyverse.org) is an umbrella package that installs several pakages that are useful for data analysis and work well together: {tidyr}, {dplyr}, {ggplot2}, {tibble}, etc.

- **{dplyr}**:  data manipulation
    - **{tidyr}**: converting between different shapes 
    - **{ggplot2}**: creating plots 
    
---

## MAIN FUNCTIONS IN `dplyr`
**Grammar** of data manipulation with very user-friendly functions for the most common tasks:
.pull-left[
<table>
<tr>
<td> Function </td> <td> Description </td> 
</tr>
<tr>
<td>

<pre>
 mutate()
</pre>

</td>
<td>

Add / modify variables

</td>
</tr>
<tr>
<td>

<pre>
 select()
</pre>

</td>
<td>

Keep / drop variables (columns)

</td>
</tr>

<pre>
 filter()
</pre>

</td>
<td>

Keep / drop observations (rows)

</td>
</tr>

<pre>
 arrange()
</pre>

</td>
<td>

Sort rows according to values of variables

</td>
</tr>

<pre>
 summarise()
</pre>

</td>
<td>

Aggregate data into descriptive statistics

</td>
</tr>
</table>]

.pull-right[
]

---

<pre>
 mutate()
</pre>

</td>
<td>

Add / modify variables

</td>
</tr>
<tr>
<td>

<pre>
 select()
</pre>

</td>
<td>

Keep / drop variables (columns)

</td>
</tr>

<pre>
 filter()
</pre>

</td>
<td>

Keep / drop observations (rows)

</td>
</tr>

<pre>
 arrange()
</pre>

</td>
<td>

Sort rows according to values of variables

</td>
</tr>

<pre>
 summarise()
</pre>

</td>
<td>

Aggregate data into descriptive statistics

</td>
</tr>
</table>]

.pull-right[The **pipe operator** ` %>% ` or `|>` (Ctrl + Shift + M)
- Allows to chain functions you apply to an object
- Take X and do this, and this, and this, and ...
<video autosize: true controls>
 <source src="data:image/png;base64,#./01_pipe_baking.mp4" type="video/mp4">
</video>
]

---
## MAIN FUNCTIONS IN `dplyr`